{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MatplotLib Tutorial\n",
    "\n",
    "Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+.\n",
    "\n",
    "Some of the major Pros of Matplotlib are:\n",
    "\n",
    "* Generally easy to get started for simple plots\n",
    "* Support for custom labels and texts\n",
    "* Great control of every element in a figure\n",
    "* High-quality output in many formats\n",
    "* Very customizable in general"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Simple Examples\n",
    "\n",
    "x=np.arange(0,10)\n",
    "y=np.arange(11,21)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.arange(40,50)\n",
    "b=np.arange(50,60)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##plotting using matplotlib \n",
    "\n",
    "##plt scatter\n",
    "\n",
    "plt.scatter(x,y,c='g')\n",
    "plt.xlabel('X axis')\n",
    "plt.ylabel('Y axis')\n",
    "plt.title('Graph in 2D')\n",
    "plt.savefig('Test.png')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=x*x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '2d Diagram')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plt plot\n",
    "\n",
    "plt.plot(x,y,'r*',linestyle='dashed',linewidth=2, markersize=12)\n",
    "plt.xlabel('X axis')\n",
    "plt.ylabel('Y axis')\n",
    "plt.title('2d Diagram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2586211cb00>]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Creating Subplots\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.plot(x,y,'r--')\n",
    "plt.subplot(2,2,2)\n",
    "plt.plot(x,y,'g*--')\n",
    "plt.subplot(2,2,3)\n",
    "plt.plot(x,y,'bo')\n",
    "plt.subplot(2,2,4)\n",
    "plt.plot(x,y,'go')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(1,11) \n",
    "y = 3 * x + 5 \n",
    "plt.title(\"Matplotlib demo\") \n",
    "plt.xlabel(\"x axis caption\") \n",
    "plt.ylabel(\"y axis caption\") \n",
    "plt.plot(x,y) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the x and y coordinates for points on a sine curve \n",
    "x = np.arange(0, 4 * np.pi, 0.1) \n",
    "y = np.sin(x) \n",
    "plt.title(\"sine wave form\") \n",
    "\n",
    "# Plot the points using matplotlib \n",
    "plt.plot(x, y) \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Subplot()\n",
    "# Compute the x and y coordinates for points on sine and cosine curves \n",
    "x = np.arange(0, 5 * np.pi, 0.1) \n",
    "y_sin = np.sin(x) \n",
    "y_cos = np.cos(x)  \n",
    "   \n",
    "# Set up a subplot grid that has height 2 and width 1, \n",
    "# and set the first such subplot as active. \n",
    "plt.subplot(2, 1, 1)\n",
    "   \n",
    "# Make the first plot \n",
    "plt.plot(x, y_sin,'r--') \n",
    "plt.title('Sine')  \n",
    "   \n",
    "# Set the second subplot as active, and make the second plot. \n",
    "plt.subplot(2, 1, 2) \n",
    "plt.plot(x, y_cos,'g--') \n",
    "plt.title('Cosine')  \n",
    "   \n",
    "# Show the figure. \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Bar plot\n",
    "\n",
    "x = [2,8,10] \n",
    "y = [11,16,9]  \n",
    "\n",
    "x2 = [3,9,11] \n",
    "y2 = [6,15,7] \n",
    "plt.bar(x, y) \n",
    "plt.bar(x2, y2, color = 'g') \n",
    "plt.title('Bar graph') \n",
    "plt.ylabel('Y axis') \n",
    "plt.xlabel('X axis')  \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27]) \n",
    "plt.hist(a) \n",
    "plt.title(\"histogram\") \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box Plot using Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [np.random.normal(0, std, 100) for std in range(1, 4)]\n",
    "\n",
    "# rectangular box plot\n",
    "plt.boxplot(data,vert=True,patch_artist=False);  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 8.18059425e-01, -1.90816017e+00, -3.58272676e-01, -2.84948168e-01,\n",
       "        -1.37184079e+00, -3.39541031e-01,  1.41745994e+00, -3.34445795e-01,\n",
       "        -5.81109988e-02, -3.39305029e-01, -1.53374508e+00, -1.15521078e+00,\n",
       "        -1.63299251e+00,  3.59415838e-01, -1.04297517e+00,  1.80662107e-01,\n",
       "        -6.23170232e-01,  4.72071230e-01,  7.35875390e-01, -4.40281463e-01,\n",
       "         4.31297134e-01, -1.78485328e-01,  1.97203111e-01,  8.73338156e-01,\n",
       "        -9.46823128e-02, -9.27014880e-01, -7.51286335e-01, -9.27487907e-01,\n",
       "        -1.12195675e+00,  1.52456530e+00,  6.86884732e-02, -4.28462451e-01,\n",
       "        -1.34901934e+00,  3.41843153e-01,  1.69940103e+00,  6.08707238e-01,\n",
       "         1.30141008e+00,  1.67039450e+00, -1.76328922e-01, -2.41419734e-02,\n",
       "         3.07284428e-02, -3.64246147e-01, -1.34149995e+00, -1.15149768e+00,\n",
       "         3.14415134e-01,  8.00201707e-01,  3.53789804e-01, -5.82668176e-01,\n",
       "         1.68353362e-03,  6.20383801e-01, -1.61138917e-01,  9.14171238e-01,\n",
       "        -6.78953867e-01, -1.76840093e+00,  5.60818198e-01,  2.03297639e+00,\n",
       "         7.27559194e-01, -7.47892251e-01,  5.31470336e-01, -6.62374134e-01,\n",
       "         1.32167015e-01, -2.58904027e-01,  1.47422460e-01, -1.41273909e+00,\n",
       "         2.36654306e-01,  4.55224454e-01, -4.62204431e-01, -5.61175485e-01,\n",
       "         2.28472581e+00, -9.14551146e-01, -2.66848774e-01, -5.61982476e-02,\n",
       "         2.36594779e+00, -2.90578180e-01,  1.03660004e+00, -1.11927992e+00,\n",
       "        -2.38413375e+00,  9.79773886e-01, -5.33459250e-03, -3.88051852e-01,\n",
       "        -1.22736698e-01,  4.48833694e-01,  4.55747578e-01, -1.60101937e+00,\n",
       "        -3.71173297e-01,  1.57013012e+00, -1.90489933e-01,  2.35167601e-01,\n",
       "        -1.23466950e+00, -7.79460123e-01,  1.12354629e+00,  7.54421640e-01,\n",
       "        -7.40836365e-01,  1.43399628e-01, -1.08592191e+00, -8.72653576e-01,\n",
       "         4.59802393e-01, -1.32898060e+00,  1.03958166e+00,  4.84857905e-01]),\n",
       " array([-2.37936628,  0.58144525,  3.17472296,  0.73461475,  2.44752705,\n",
       "        -0.58539574,  1.70692434, -0.31761251, -4.16113791,  1.9073468 ,\n",
       "         0.07192839,  0.39920154, -1.8106135 ,  1.6209514 ,  1.34670526,\n",
       "         2.27141693,  1.62871999,  1.24002886,  0.26745409, -4.17201664,\n",
       "        -0.38003674, -0.28487538,  1.16777349, -1.39788391, -0.34897194,\n",
       "         0.4634663 , -0.17512152, -1.03894001,  0.44666292,  0.02742949,\n",
       "         3.85377592, -2.60482224, -0.17669632, -1.3373201 , -0.6647066 ,\n",
       "         2.58377247, -3.1390957 ,  2.97647579, -3.69391364,  0.24887292,\n",
       "         1.36942645,  1.90402939, -0.33876593,  0.40674037,  2.61500912,\n",
       "         2.03364845, -1.73694401, -1.67078949, -1.76682762,  3.36716599,\n",
       "        -0.64653285,  1.60442354,  0.86645246, -2.8901474 , -2.23856972,\n",
       "         3.66936878, -2.5202765 , -5.5299137 ,  3.26220184,  2.4394264 ,\n",
       "         0.32615799,  0.46785319,  3.33078595, -0.46421557, -0.45566686,\n",
       "         1.35158859,  1.52216112, -5.23021247,  1.68431896, -3.06730713,\n",
       "        -2.30073038, -1.47486285, -0.903597  , -1.04871434,  1.94672994,\n",
       "        -1.07848787,  0.18774314, -1.05445398, -1.46412603,  0.2298612 ,\n",
       "        -3.26239396, -0.52410778,  3.07746796,  2.83131338,  1.28559715,\n",
       "        -1.43048493,  1.06336789, -1.23793218, -0.8753594 ,  1.1699117 ,\n",
       "         1.26773603, -4.06354555, -0.58927352,  0.57131086, -1.85553899,\n",
       "         0.68648421, -3.25054767, -2.20296286, -2.51065221,  0.33475013]),\n",
       " array([ 3.23472263e+00, -7.79575238e+00,  1.64937220e+00,  1.76399179e+00,\n",
       "         1.22128560e+00,  3.29208487e+00, -3.11706599e+00,  9.41212157e-01,\n",
       "        -8.74167203e-01,  4.12408305e+00,  2.02902288e+00, -7.49153273e-02,\n",
       "         5.91872076e+00,  2.11476276e+00, -8.45806080e+00, -5.21408210e+00,\n",
       "        -2.60667103e+00, -5.80932482e-03,  6.26762992e+00, -1.39187127e+00,\n",
       "        -1.64145704e-01, -2.82070599e+00,  3.08699676e+00,  2.49864911e+00,\n",
       "         1.63658633e+00,  7.66204291e-01, -3.26074646e-01, -2.17537513e+00,\n",
       "         1.15145693e+00, -2.89751786e+00,  2.28322556e+00,  6.19642521e+00,\n",
       "        -1.82927106e+00, -1.09345798e+00,  3.11151381e+00,  1.12017604e+00,\n",
       "         9.41012231e-01, -6.81613313e+00,  3.10526399e+00,  3.85926057e+00,\n",
       "         4.42743765e-02,  4.52471202e-01,  3.93258713e+00, -2.18658857e+00,\n",
       "         2.11792258e-01,  4.61258001e-01,  8.41864901e-01,  6.30780033e+00,\n",
       "        -1.31348489e+00, -2.40983306e+00,  4.50718827e+00,  6.03292889e-01,\n",
       "         1.28718269e+00, -1.80621446e-01,  4.28063685e+00,  3.97739704e+00,\n",
       "         4.22216815e+00, -1.79345502e-01,  1.23860217e+00, -4.79008661e+00,\n",
       "        -2.34775048e+00,  3.39419243e+00, -3.92874523e+00, -3.78309833e-01,\n",
       "        -9.54131887e-01,  5.22225967e+00, -1.31996058e+00, -1.78132117e-01,\n",
       "        -1.30243447e+00,  5.37710749e+00, -1.18192345e+00,  8.03094267e-01,\n",
       "        -5.44421416e+00, -4.55512394e+00,  1.88393062e+00,  1.93751347e+00,\n",
       "         3.62403154e+00, -4.26299753e-01, -8.40413680e-01,  3.27710075e+00,\n",
       "        -5.43921858e-01,  1.61884951e+00,  2.84600391e+00,  2.11396873e+00,\n",
       "         1.85807216e+00,  1.49022456e+00, -2.19338031e+00,  2.37455891e+00,\n",
       "         2.05891999e+00, -9.67238160e-02,  1.34032864e-01,  1.68297511e+00,\n",
       "        -3.18440833e+00,  4.51061952e-02,  2.41432422e+00,  1.03056513e+00,\n",
       "        -3.67428744e+00, -8.38452960e-01,  2.84590424e+00, -5.49424695e+00])]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pie Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data to plot\n",
    "labels = 'Python', 'C++', 'Ruby', 'Java'\n",
    "sizes = [215, 130, 245, 210]\n",
    "colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']\n",
    "explode = (0.4, 0, 0, 0)  # explode 1st slice\n",
    "\n",
    "# Plot\n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
    "autopct='%1.1f%%', shadow=False)\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
